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            <h1>GPT ネオックスの llm.int ()</h1>
<p>これは、レイヤーを llm.int8 () <code  class="highlight"><span></span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></code>
 線形レイヤーに変換するユーティリティ関数を実装します。</p>
<p><a href="https://papers.labml.ai/paper/eb2bcaee1d0011edaa66a71c10a887e7">LLM.int8 () の論文では</a>、大規模な言語モデルでパフォーマンスを低下させることなく、外れ値を処理する際に int8 量子化を使用してメモリ使用量を削減できることが示されています。重みと入力をスケーリングされた8ビット整数に変換し、行列乗算を行ってint32の結果を生成し、それをfloat16に変換して再スケーリングします。その結果、大規模な言語モデルでは、一部の特徴によって極端な値 (外れ値) がモデルの出力の大部分を占める可能性があることが示されています。これらの機能は8ビットの整数空間に制限されるため、モデルのパフォーマンスが低下します。解決策として、これらの外れ値（指定されたしきい値を超える）を選択し、その乗算をfloat16空間で個別に計算します。外れ値の割合は約 0.01% なので、メモリ使用量は増加せず、モデルのパフォーマンス低下を防ぎます</p>。
<p><a href="../model.html#post_load_prepare">GPT-NoEx レイヤーを変換するコードは model.py で定義されています。</a></p>
<p>int8 量子化による GPT-Neox の使用例を以下に示します。</p>
<ul><li><a href="../samples/llm_int8.html">テキストを生成</a></li>
<li><a href="../evaluation/llm_int8.html">評価テストを実行</a></li></ul>

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            <div class="highlight"><pre><span class="lineno">33</span><span></span></pre></div>
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            <p><a href="https://github.com/timdettmers/bitsandbytes"><code  class="highlight"><span></span><span class="n">bitsandbytes</span></code>
</a>パッケージをインポート</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">34</span><span class="k">try</span><span class="p">:</span>
<span class="lineno">35</span>    <span class="kn">from</span> <span class="nn">bitsandbytes.nn</span> <span class="kn">import</span> <span class="n">Linear8bitLt</span><span class="p">,</span> <span class="n">Int8Params</span>
<span class="lineno">36</span><span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
<span class="lineno">37</span>    <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s1">&#39;&#39;&#39;Please install `bitsandbytes` with `pip install bitsandbytes -U`&#39;&#39;&#39;</span><span class="p">)</span>
<span class="lineno">38</span>
<span class="lineno">39</span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">40</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span></pre></div>
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            <h2><code  class="highlight"><span></span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></code>
レイヤーを llm.int8 () リニアレイヤーに変換します</h2>
<ul><li><code  class="highlight"><span></span><span class="n">linear_module</span></code>
<code  class="highlight"><span></span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></code>
変換するレイヤーです</li>
<li><code  class="highlight"><span></span><span class="n">device</span></code>
モデルのデバイスです</li>
<li><code  class="highlight"><span></span><span class="n">threshold</span></code>
<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span></span></span></span></span>外れ値の検出に使用する閾値です</li></ul>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">43</span><span class="k">def</span> <span class="nf">make_llm_int8_linear</span><span class="p">(</span><span class="n">linear_module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">6.0</span><span class="p">):</span></pre></div>
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            <p></p>

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            <div class="highlight"><pre><span class="lineno">53</span>    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">linear_module</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">)</span></pre></div>
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            <p>空の Linear8BitLT モジュールを作成します。</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">56</span>    <span class="n">int8_lin</span> <span class="o">=</span> <span class="n">Linear8bitLt</span><span class="p">(</span>
<span class="lineno">57</span>        <span class="n">linear_module</span><span class="o">.</span><span class="n">in_features</span><span class="p">,</span>
<span class="lineno">58</span>        <span class="n">linear_module</span><span class="o">.</span><span class="n">out_features</span><span class="p">,</span>
<span class="lineno">59</span>        <span class="n">linear_module</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span>
<span class="lineno">60</span>        <span class="n">has_fp16_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="lineno">61</span>        <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
<span class="lineno">62</span>    <span class="p">)</span></pre></div>
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            <p>ウェイトをクオンタイズ</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">65</span>    <span class="n">int8_lin</span><span class="o">.</span><span class="n">_parameters</span><span class="p">[</span><span class="s1">&#39;weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Int8Params</span><span class="p">(</span><span class="n">linear_module</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span>
<span class="lineno">66</span>                                                <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="lineno">67</span>                                                <span class="n">has_fp16_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></pre></div>
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            <p>float16 空間にバイアスを設定します。</p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">70</span>    <span class="k">if</span> <span class="n">linear_module</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="lineno">71</span>        <span class="n">int8_lin</span><span class="o">.</span><span class="n">_parameters</span><span class="p">[</span><span class="s1">&#39;bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">linear_module</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span>
<span class="lineno">72</span>                                                    <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></pre></div>
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            <p></p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">75</span>    <span class="k">return</span> <span class="n">int8_lin</span></pre></div>
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